We use Adam (Adaptive Moment Estimation) algorithm to minimize the loss function [1]}. Furthermore, we use an unsupervised ML approach (UMAP) to gain more insights into the predicted correlation matrices. The UMAP (Uniform Manifold Approximation and Projection) is a manifold learning technique for dimension reduction [2]}. The method preserves the similarity between the vectors in the higher dimension and performs a nonlinear projection to the 2 dimensional space. The UMAP preserves the local and global structures of the data set. Data having similar structures or features are clustered together in low dimensions. The use of UMAP here is two-fold – (a) provides a visual understanding of the predicted correlation matrices with the true correlation matrices, and (b) helps us to select the appropriate training data set to get a meaningful prediction (SI sec. 4).
